Interference effects from machine learning matched confusable object pairs in memory assessment tasks - Batra, D (2022)
For early detection of neurodegeneration, objective cognitive tests provide a capable alternative to costly and invasive traditional biomarkers, as well as to short and infrequent in-clinic cognitive assessments. These tests, that are designed to target the cognitive processes first vulnerable to change in AD, can be remotely deployed in individuals’ lives over long durations for frequent and repeated measurements using widely accessible digital devices such as smartphones. A class of such objective tests loads on the susceptibility of the short-term memory to interference from perceptually similar objects to obtain the first signal of AD-specific neurodegeneration. Evidence suggests that increasing the similarity of the confusable object pairs used in such tasks could have differential impact on the ability of individuals at different stages of cognitive health to inhibit interference from such pairs, thereby making object pair similarity a useful metric in sensitively recording individual cognitive differences. While this claim has been tested across various stimulus pair types, due to the lack of standardised datasets of confusable object pairs parametrically varying in similarity where the constituent objects in the pairs differ conceptually but still share some degree of similarity (e.g., guitar and banjo), such an assessment remains unexplored for this stimulus type. In this thesis, this gap is addressed by first using machine learning based models of vision and semantics, as well as their combinations, to produce the required datasets of object pairs parametrically varying in similarity, followed by exploring the differential effects of such confusable object pairs on interference in short-term memory among participants from varied demographics in an objective test of cognition.